cim: Clustered Image Maps (CIMs) ("heat maps")

Description

This function generates color-coded Clustered Image Maps (CIMs) ("heat maps") to represent "high-dimensional" data sets.

Usage

cim(      mat,
           color = NULL,
           row.names = TRUE,
           col.names = TRUE,
           row.sideColors = NULL,
           col.sideColors = NULL,
           row.cex = NULL,
           col.cex = NULL,
           cluster = "both",
           dist.method = c("euclidean", "euclidean"),
           clust.method = c("complete", "complete"),
           cut.tree = c(0, 0),
           transpose = FALSE,
           comp = NULL,
           symkey = TRUE, 
           keysize = c(1, 1),            
           zoom = FALSE, 
           main = NULL,
           xlab = NULL,
           ylab = NULL,
           margins = c(5, 5),
           lhei = NULL,
           lwid = NULL,
           sample.names = TRUE,
           var.names = TRUE,
           sample.sideColors = NULL,
           var.sideColors = NULL,
           center = TRUE,
           scale = FALSE,
           X.var.names = TRUE, 
           Y.var.names = TRUE,
           x.sideColors = NULL,
           y.sideColors = NULL,
           mapping = "XY",
           legend=NULL,
           ...)

Arguments

mat

numeric matrix of values to be plotted. Alternatively, an object of class inheriting from "pca", "spca", "ipca", "sipca", "rcc", "pls", "spls", "plsda"

color

a character vector of colors such as that generated by terrain.colors, topo.colors, rainbow,

row.names, col.names

logical, should the name of rows and/or columns of mat be shown? If TRUE (defaults) rownames(mat) and/or colnames(mat) are used. Possible character vectors with row and/or column labels to use.

sample.names, var.names

logical, should the name of samples and/or variables be shown? If TRUE (defaults) object$names$indiv and/or object$names$X are used. Possible character vector with sample and/or variable labels to use.

X.var.names, Y.var.names

logical, should the name of $X$- and/or $Y$-variables be shown? If TRUE (defaults) object$names$X and/or object$names$Y are used. Possible character vector with $X$- and/or $Y$-variable labels to use.

row.sideColors

(optional) character vector of length nrow(mat) containing the color names for a vertical side bar that may be used to annotate the rows of mat.

col.sideColors

(optional) character vector of length ncol(mat) containing the color names for a horizontal side bar that may be used to annotate the columns of mat.

sample.sideColors

(optional) character vector of length nrow(object$X) containing the color names for a vertical side bar that may be used to annotate the samples.

var.sideColors

(optional) character vector of length ncol(object$X) containing the color names for a horizontal side bar that may be used to annotate the variables.

x.sideColors, y.sideColors

(optional) character vector of length ncol(object$X) and ncol(object$Y) containing the color names for horizontal and vertical side bars that may be used to annotate the $X$- and/or $Y$-variables.

row.cex, col.cex

positive numbers, used as cex.axis in for the row or column axis labeling. The defaults currently only use number of rows or columns, respectively.

mapping

character string indicating whether to map "X", "Y" or "XY"-association matrix. See Details.

cluster

character string indicating whether to cluster "none", "row", "column" or "both". Defaults to "both".

dist.method

character vector of length two. The distance measure used in clustering rows and columns. Possible values are "correlation" for Pearson correlation and all the distances supported by dist,

clust.method

character vector of length two. The agglomeration method to be used for rows and columns. Accepts the same values as in hclust such as "ward", "complete", etc.

cut.tree

numeric vector of length two with components in [0,1]. The height proportions where the trees should be cut for rows and columns, if these are clustered.

comp

atomic or vector of positive integers. The components to adequately account for the data association. For a non sparse method, the similarity matrix is computed based on the variates and loading vectors of those specified components. For a sparse app

transpose

logical indicating if the matrix should be transposed for plotting. Defaults to FALSE.

center

either a logical value or a numeric vector of length equal to the number of columns of mat. See scale function.

scale

either a logical value or a numeric vector of length equal to the number of columns of mat. See scale function.

symkey

boolean indicating whether the color key should be made symmetric about 0. Defaults to TRUE.

keysize

vector of length two, indicating the size of the color key.

zoom

logical. Whether to use zoom for interactively zooming-out. See Details.

main, xlab, ylab

main, $x$- and $y$-axis titles; defaults to none.

margins

numeric vector of length two containing the margins (see par(mar)) for column and row names respectively.

lhei, lwid

arguments passed to layout to divide the device up into two (or three if a side color is drawn) rows and two columns, with the row-heights lhei and the column-widths lwid.

legend

A list indicating the legend for each group, the color vector, title of the legend and cex. For the other objects See examples and our website.

...

arguments passed to cim.

Value

A list containing the following components:
Mthe mapped matrix used by cim.
rowInd, colIndrow and column index permutation vectors as returned by order.dendrogram.
ddr, ddcobject of class "dendrogram" which describes the row and column trees produced by cim.
row.names, col.namescharacter vectors with row and column labels used.
row.sideColors, col.sideColorscharacter vector containing the color names for vertical and horizontal side bars used to annotate the rows and columns.

encoding

latin1

Details

One matrix Clustered Image Map (default method) is a 2-dimensional visualization of a real-valued matrix (basically image(t(mat))) with rows and/or columns reordered according to some hierarchical clustering method to identify interesting patterns. Generated dendrograms from clustering are added to the left side and to the top of the image. By default the used clustering method for rows and columns is the complete linkage method and the used distance measure is the distance euclidean.

In "pca", "spca", "ipca", "sipca", "plsda", "splsda" and "mlsplsda" methods the mat matrix is object$X.

For the remaining methods, if mapping = "X" or mapping = "Y" the mat matrix is object$X or object$Y respectively. If mapping = "XY":

inrccmethod, the matrixmatis created where element$(j,k)$is the scalar product value between every pairs of vectors in dimensionlength(comp)representing the variables$X_j$and$Y_k$on the axis defined by$Z_i$with$i$incomp, where$Z_i$is the equiangular vector between the$i$-th$X$and$Y$canonical variate.
inpls,splsandmlsplsmethods, ifobject$modeis"regression", the element$(j,k)$of the matrixmatis given by the scalar product value between every pairs of vectors in dimensionlength(comp)representing the variables$X_j$and$Y_k$on the axis defined by$U_i$with$i$incomp, where$U_i$is the$i$-th$X$variate. Ifobject$modeis"canonical"then$X_j$and$Y_k$are represented on the axis defined by$U_i$and$V_i$respectively.

By default four components will be displayed in the plot. At the top left is the color key, top right is the column dendogram, bottom left is the row dendogram, bottom right is the image plot. When sideColors are provided, an additional row or column is inserted in the appropriate location. This layout can be overriden by specifiying appropriate values for lwid and lhei. lwid controls the column width, and lhei controls the row height. See the help page for layout for details on how to use these arguments.

For visualization of "high-dimensional" data sets, a nice zooming tool was created. zoom = TRUE open a new device, one for CIM, one for zoom-out region and define an interactive 'zoom' process: click two points at imagen map region by pressing the first mouse button. It then draws a rectangle around the selected region and zoom-out this at new device. The process can be repeated to zoom-out other regions of interest.

The zoom process is terminated by clicking the second button and selecting 'Stop' from the menu, or from the 'Stop' menu on the graphics window.

References

Eisen, M. B., Spellman, P. T., Brown, P. O. and Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proceeding of the National Academy of Sciences of the USA 95, 14863-14868.

Weinstein, J. N., Myers, T. G., O'Connor, P. M., Friend, S. H., Fornace Jr., A. J., Kohn, K. W., Fojo, T., Bates, S. E., Rubinstein, L. V., Anderson, N. L., Buolamwini, J. K., van Osdol, W. W., Monks, A. P., Scudiero, D. A., Sausville, E. A., Zaharevitz, D. W., Bunow, B., Viswanadhan, V. N., Johnson, G. S., Wittes, R. E. and Paull, K. D. (1997). An information-intensive approach to the molecular pharmacology of cancer. Science 275, 343-349.

Gonzalez I., Le Cao K.A., Davis M.J., Dejean S. (2012). Visualising associations between paired 'omics' data sets. BioData Mining; 5(1).

Examples

Run this code

## default method: shwos cross correlation between 2 data sets
#------------------------------------------------------------------
data(nutrimouse)
X <- nutrimouse$lipid
Y <- nutrimouse$gene
  
cim(cor(X, Y), cluster = "none")
  
  
## CIM representation for objects of class 'rcc'
#------------------------------------------------------------------
nutri.rcc <- rcc(X, Y, ncomp = 3, lambda1 = 0.064, lambda2 = 0.008)

cim(nutri.rcc, xlab = "genes", ylab = "lipids", margins = c(5, 6))

#-- interactive 'zoom' available as below
cim(nutri.rcc, xlab = "genes", ylab = "lipids", margins = c(5, 6), 
    zoom = TRUE)
#-- select the region and "see" the zoom-out region, click on 'finish' or 'exit' to get out
# Rstudio might throw a warning message.

#-- cim from X matrix with a side bar to indicate the diet
diet.col <- palette()[as.numeric(nutrimouse$diet)]
cim(nutri.rcc, mapping = "X", sample.names = nutrimouse$diet,
    sample.sideColors = diet.col, xlab = "lipids",
    clust.method = c("ward", "ward"), margins = c(6, 4))

#-- cim from Y matrix with a side bar to indicate the genotype
geno.col = color.mixo(as.numeric(nutrimouse$genotype))
cim(nutri.rcc, mapping = "Y", sample.names = nutrimouse$genotype,
    sample.sideColors = geno.col, xlab = "genes",
    clust.method = c("ward", "ward"))

## CIM representation for objects of class 'spca' (also works for sipca)
#------------------------------------------------------------------
data(liver.toxicity)
X <- liver.toxicity$gene

liver.spca <- spca(X, ncomp = 2, keepX = c(30, 30), scale = FALSE)

dose.col <- color.mixo(as.numeric(as.factor(liver.toxicity$treatment[, 3])))

# side bar, no variable names shown
cim(liver.spca, sample.sideColors = dose.col, var.names = FALSE,
    sample.names = liver.toxicity$treatment[, 3],
    clust.method = c("ward", "ward"))  

## CIM representation for objects of class '(s)pls' 
#------------------------------------------------------------------
data(liver.toxicity)

X <- liver.toxicity$gene
Y <- liver.toxicity$clinic
liver.spls <- spls(X, Y, ncomp = 3,
                      keepX = c(20, 50, 50), keepY = c(10, 10, 10))


# default
cim(liver.spls)

# transpose matrix, choose clustering method
cim(liver.spls, transpose = TRUE,   
    clust.method = c("ward", "ward"), margins = c(5, 7))

# Here we visualise only the X variables selected 
cim(liver.spls, mapping="X")

# Here we should visualise only the Y variables selected
cim(liver.spls, mapping="Y") 

# Here we only visualise the similarity matrix between the variables by spls  
cim(liver.spls, cluster="none")

# plotting two data sets with the similarity matrix as input in the funciton 
# (see our BioData Mining paper for more details)
# Only the variables selected by the sPLS model in X and Y are represented
cim(liver.spls, mapping="XY")

# on the X matrix only, side col var to indicate dose
dose.col <- color.mixo(as.numeric(as.factor(liver.toxicity$treatment[, 3])))
cim(liver.spls, mapping = "X", sample.sideColors = dose.col, 
    sample.names = liver.toxicity$treatment[, 3])


# CIM default representation includes the total of 120 genes selected, with the dose color
# with a sparse method, show only the variables selected on specific components
cim(liver.spls, comp = 1)
cim(liver.spls, comp = 2)
cim(liver.spls, comp = c(1,2))
cim(liver.spls, comp = c(1,3))


## CIM representation for objects of class '(s)plsda' 
#------------------------------------------------------------------
# Setting up the Y outcome first
Y <- liver.toxicity$treatment[, 3]

liver.splsda <- splsda(X, Y, ncomp = 2, keepX = c(40, 30))

cim(liver.splsda, sample.sideColors = dose.col, sample.names = Y)

## CIM representation for objects of class splsda 'multilevel' 
# with a two level factor (repeated sample and time)
#------------------------------------------------------------------
data(vac18.simulated)
X <- vac18.simulated$genes
design <- data.frame(samp = vac18.simulated$sample,
                     time = vac18.simulated$time,
                     stim = vac18.simulated$stimulation)

res.2level <- multilevel(X, ncomp = 2, design = design,
                         keepX = c(120, 10), method = 'splsda')

#define colors for the levels: stimulation and time
stim.col <- c("darkblue", "purple", "green4","red3")
stim.col <- stim.col[as.numeric(design$stim)]
time.col <- c("orange", "cyan")[as.numeric(design$time)]


# The row side bar indicates the two levels of the facteor, stimulation and time.
# the sample names have been motified on the plot.
cim(res.2level, sample.sideColors = cbind(stim.col, time.col), 
    sample.names = paste(design$time, design$stim, sep = "_"),
    var.names = FALSE,
  #setting up legend:
    legend=list(legend = c(levels(design$time), levels(design$stim)), 
                col = c("orange", "cyan", "darkblue", "purple", "green4","red3"), 
                title = "Condition", cex = 0.7)
)


## CIM representation for objects of class spls 'multilevel' 
#------------------------------------------------------------------
data(liver.toxicity)
repeat.indiv <- c(1, 2, 1, 2, 1, 2, 1, 2, 3, 3, 4, 3, 4, 3, 4, 4, 5, 6, 5, 5,
                  6, 5, 6, 7, 7, 8, 6, 7, 8, 7, 8, 8, 9, 10, 9, 10, 11, 9, 9,
                  10, 11, 12, 12, 10, 11, 12, 11, 12, 13, 14, 13, 14, 13, 14,
                  13, 14, 15, 16, 15, 16, 15, 16, 15, 16)

# sPLS is a non supervised technique, and so we only indicate the sample repetitions 
# in the design (1 factor only here, sample)
# sPLS takes as an input 2 data sets, and the variables selected
design <- data.frame(sample = repeat.indiv) 
res.spls.1level <- multilevel(X = liver.toxicity$gene,
                              Y=liver.toxicity$clinic,
                              design = design,
                              ncomp = 2,
                              keepX = c(50, 50), keepY = c(5, 5),
                              method = 'spls', 
                              mode = 'canonical')

stim.col <- c("darkblue", "purple", "green4","red3")

# showing only the Y variables, and only those selected in comp 1 
cim(res.spls.1level, mapping="Y",
    sample.sideColors = stim.col[factor(liver.toxicity$treatment[,3])], comp = 1,
    #setting up legend:
    legend=list(legend = unique(liver.toxicity$treatment[,3]), col=stim.col, 
    title = "Dose", cex=0.9))


# showing only the X variables, for all selected on comp 1 and 2 
cim(res.spls.1level, mapping="X",
    sample.sideColors = stim.col[factor(liver.toxicity$treatment[,3])], 
    #setting up legend:
    legend=list(legend = unique(liver.toxicity$treatment[,3]), col=stim.col, 
    title = "Dose", cex=0.9))


# These are the cross correlations between the variables selected in X and Y.
# The similarity matrix is obtained as in our paper in Data Mining
cim(res.spls.1level, mapping="XY")

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